The philosophical basis of algorithmic recourse
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Philosophers have established that certain ethically important values are modally robust in the sense that they systematically deliver correlative benefits across a range of counterfactual scenarios. In this paper, we contend that recourse - the systematic process of reversing unfavorable decisions by algorithms and bureaucracies across a range of counterfactual scenarios - is such a modally robust good. In particular, we argue that two essential components of a good life - temporally extended agency and trust - are underwritten by recourse.
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Algorithmic recourse and contestability both aim to reverse decisions; in recourse, the individual perceives the decision as correct, whereas in contestability, it is perceived as incorrect.
“Both algorithmic recourse and contestability aim to reverse decisions; however, in recourse, the individual perceives the decision as correct, whereas in contestability, it is perceived as incorrect (Venkatasubramanian and Alfano 2020).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Venkatasubramanian and Alfano 2020
Section 3.1 distinguishes recourse narrowly conceived, which seeks a better outcome after a correct-but-unfavorable decision, from appeal, which corrects a decision based on faulty or incomplete data. This supports the broad contrast, but the paper describes whether the decision is actually based on correct data, not whether the individual perceives it as correct, and calls the second process appeal rather than contestability. No page number is available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredFor purposes of contestability, maximal proximity is less essential than it is for recourse.
“For purposes of contestability, maximal proximity is less essential than it is for recourse(Venkatasubramanian and Alfano 2020;Freiesleben 2022).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Venkatasubramanian and Alfano 2020
Section 2 says counterfactual explanations can provide information for contesting decisions and exercising recourse; §3.1 distinguishes appeal from recourse, and §4.6 discusses the least burdensome option for recourse. These passages support a distinction between the purposes, but they do not say that maximal proximity is less essential for contestability than for recourse. Page numbers are unavailable in our copy. (LLM, conf=0.72)
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Cites
On the (im)possibility of fairness
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Cited by
Sorted by citations
The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making
de Bruijn, H., Warnier, M., & Janssen, M. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2021.101666 · Cited by 264 · see citing works
Problems with Shapley-value-based explanations as feature importance measures
Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. (2020) · arXiv · doi:10.48550/arxiv.2002.11097 · Cited by 228 · see citing works
“This setup has been formalized as the “counterfactual explanation” problem by Wachter et al. (2017) (with an analysis of hidden assumptions by Barocas et al.”
MethodSelf-citationYurrita, M., Draws, T., Balayn, A., Murray-Rust, D., Tintarev, N., & Bozzon, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581161 · Cited by 73 · see citing works
“Contesting a decision has been defned as the act of opposing an action; either because the action is perceived as mistaken or simply wrong [4, 99].”
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Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions
Lyons, H., Velloso, E., & Miller, T. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449180 · Cited by 4 · see citing works
Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
Freiesleben, T., Meding, K., & König, G. (2026) · arXiv · doi:10.48550/arxiv.2605.16041
- Authors
- Venkatasubramanian, S., & Alfano, M.
- Affiliations
- University of Utah (US)
- Macquarie University (AU)
- Venue
- Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2020-01-27
- Pages
- 284-293
- OpenAlex
- W3003166972
- Cited by
- 114
- References
- 1
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 13 · 2023: 22 · 2022: 17 · 2021: 26 · 2020: 10
- FWCI
- 18.97
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Funders
- NSF (IIS-1633724)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Arts and Humanities › Philosophy › Philosophical Ethics and Theory
- Keywords
- algorithmic recourse, counterfactual reasoning, trust, algorithmic decision-making, bureaucratic decision-making, algorithmic ethics, good life
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